New book

[Thomas Petsche]

The following book is now available from MIT Press:

COMPUTATIONAL LEARNING THEORY AND NATURAL LEARNING SYSTEMS
  Volume II: Intersection between Theory and Experiments

Edited by Stephen J. Hanson, Thomas Petsche, Michael Kearns, and
  Ronald L. Rivest.


The book is the result of a workshop of the same name which brought
together researchers from learning theory, machines learning, and
neural networks.  The book includes 23 chapters by authors in these
various fields plus a unified bibliography and index:

 1. Bayes Decisions in a Neural Network-PAC Setting
    Svetlana Anulova, Jorge R. Cuellar, Klaus-U. Hoeffgen and Hans-U. Simon

 2. Average Case Analysis of $k$-CNF and $k$-DNF Learning Algorithms 
    Daniel S. Hirschberg, Michael J. Pazzani and Kamal M. Ali

 3. Filter Likelihoods and Exhaustive Learning
    David H. Wolpert

 4. Incorporating Prior Knowledge into Networks of Locally-Tuned Units
    Martin Roescheisen, Reimar Hofmann and Volker Tresp

 5. Using Knowledge-Based Neural Networks to Refine Roughly-Correct
    Information 
    Geoffrey G. Towell and Jude W. Shavlik

    6. Sensitivity Constraints in Learning
    Scott H. Clearwater and Yongwon Lee

 7. Evaluation of Learning Biases Using Probabilistic Domain Knowledge
    Marie desJardins

 8. Detecting Structure in Small Datasets by Network Fitting under
    Complexity Constraints 
    W. Finnoff and H.G. Zimmermann

 9. Associative Methods in Reinforcement Learning: An Empirical Study
    Leslie Pack Kaelbling

10. A Schema for Using Multiple Knowledge
    Matjaz Gams, Marko Bohanec and Bojan Cestnik

11. Probabilistic Hill-Climbing
    William W. Cohen, Russell Greiner and Dale Schuurmans

12. Prototype Selection Using Competitive Learning
    Michael Lemmon

13. Learning with Instance-Based Encodings
    Henry Tirri

14. Contrastive Learning with Graded Random Networks
    Javier R. Movellan and James L. McClelland

15. Probability Density Estimation and Local Basis Function Neural Networks
    Padhraic Smyth

16. Hamiltonian Dynamics of Neural Networks
    Ulrich Ramacher

17. Learning Properties of Multi-Layer Perceptrons with and without
    Feedback  
    D. Gawronska, B. Schuermann and J. Hollatz

18. Unsupervised Learning for Mobile Robot Navigation Using
    Probabilistic Data Association 
    Ingemar J. Cox and John J. Leonard

19. Evolution of a Subsumption Architecture that Performs a Wall
    Following Task for an Autonomous Mobile Robot 
    John R. Koza

20. A Connectionist Model of the Learning of Personal Pronouns in
    English 
    Thomas R. Shultz, David Buckingham and Yuriko Oshima-Takane

21. Neural Network Modeling of Physiological Processes
    Volker Tresp, John Moody and Wolf-Ruediger Delong

22. Projection Pursuit Learning: Some Theoretical Issues
    Ying Zhao and Christopher G. Atkeson

23. A Comparative Study of the Kohonen Self-Organizing Map and the
    Elastic Net 
    Yiu-fai Wong


The book is ISBN 0-262-58133-7 and the price is $35 (I believe).  
Additional ordering information can be obtained from:
	Neil Blaisdell
	MIT/Bradford Books
	Sales Department
	blaisdel at mit.edu
(They will take a credit card order if you like (and trust the
net with you credit card number).)